{"id":"W2830690711","doi":"10.1111/age.12677","title":"Accuracy of genotype imputation in Labrador Retrievers","year":2018,"lang":"en","type":"article","venue":"Animal Genetics","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Biotechnology and Biological Sciences Research Council; Dogs Trust; Rural and Environment Science and Analytical Services Division","keywords":"Imputation (statistics); Genotyping; Biology; SNP; Genotype; Single-nucleotide polymorphism; Labrador Retriever; SNP genotyping; Genetics; Genome-wide association study; Statistics; Missing data; Gene; Mathematics; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01917903,0.0004651434,0.00109945,0.000539413,0.0004006966,0.001384723,0.001087761,0.001082338,0.001196887],"category_scores_gemma":[0.02480093,0.0002760763,0.0010339,0.0009234141,0.0006372025,0.0005614113,0.00064954,0.0004924881,0.0004635529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008431218,"about_ca_system_score_gemma":0.0005899503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009535005,"about_ca_topic_score_gemma":0.00666975,"domain_scores_codex":[0.9924413,0.005095599,0.0003532503,0.001343793,0.0004528514,0.0003131743],"domain_scores_gemma":[0.9799226,0.01394335,0.00169474,0.003155992,0.001074871,0.0002084962],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002759906,0.0001300311,0.4904886,0.0001568944,0.001676084,0.0005519115,0.0006075698,0.4426169,0.006348153,0.003174676,0.001674328,0.04981498],"study_design_scores_gemma":[0.0001899371,0.0005376023,0.3040111,0.0001244384,0.0005887645,0.0004907821,0.0002885242,0.6790538,0.007860651,0.00369207,0.003035208,0.0001270431],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9683158,0.0006199383,0.02779795,0.0002009162,0.00001732181,0.0000332145,0.001398232,0.0002690869,0.001347596],"genre_scores_gemma":[0.9911687,0.00008557062,0.006369445,0.00006342099,0.000005309207,0.00002696438,0.001815562,0.00003510576,0.0004299009],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01917903,"threshold_uncertainty_score":0.1014296,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01250290723344608,"score_gpt":0.2676988958284348,"score_spread":0.2551959885949887,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}